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| title: FtsI Classifier | |
| emoji: 📈 | |
| colorFrom: gray | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 6.1.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Finding unique k-mer signatures among groups of sequences | |
| # Genomic Classification of Beta-Lactam Resistance | |
| This repository provides a Python implementation of a genomic classification framework based on k-mer analysis of gene sequences (ftsI / penA) to predict beta-lactam susceptibility categories. | |
| ## Description | |
| The method identifies group-specific k-mers from allele sequences and uses them to classify isolates into resistance categories. The approach is designed to capture genomic patterns beyond predefined mutation rules. | |
| This framework was developed and applied to: | |
| - *Haemophilus influenzae* (ftsI gene) | |
| - *Neisseria meningitidis* (penA gene) | |
| ## Files | |
| - `app.py` — main application (Gradio interface) | |
| - `kmer_unique.py` — core k-mer extraction and filtering functions | |
| - `requirements.txt` — Python dependencies | |
| ## Requirements | |
| Python ≥ 3.9 | |
| Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ## Usage | |
| ### Hugging Face interface | |
| Run the app directly via the Hugging Face Space interface. | |
| ## Input | |
| - FASTA file containing gene sequences (ftsI or penA) | |
| - Sequences grouped by phenotype or category | |
| ## Output | |
| - Predicted resistance category per sequence | |
| - Identification of group-specific k-mer signatures | |
| ## Method overview | |
| 1. Extraction of k-mers from sequences | |
| 2. Identification of unique k-mers per category | |
| 3. Filtering based on frequency thresholds | |
| 4. Classification of sequences based on k-mer matches | |
| ## Citation | |
| If you use this code, please cite: | |
| [to be added manuscript reference] | |
| ## DOI | |
| DOI: (10.5281/zenodo.19665426) |